Deep learning approaches to inpainting are more globally consistent and achieve better local details. Many of these take the form of encoder-decoder CNNs, such as U-Net [1]} or GAN-based algorithms [2]}. U-Net consists of a U-shaped CNN that first downsamples the input image using convolutions and pooling layers, and then upsamples using transposed convolutions and skip-connections. Neural Path Synthesis [3]} enhances the GAN-based approach with a texture network inspired by style transfer to transfer the style of the context pixels to improve local details. Multi-scale discriminators have been employed as well to capture global and local details [4]}, [5]}.
